In order to facilitate comparison, our experiments are conducted on the FF++ [1]} dataset.
FF++ is a large-scale video dataset consisting of 1000 original videos that have been manipulated
by four face manipulation methods:
DeepFakes [2]}, Face2Face [3]}, FaceSwap[4]}, and NeuralTextures [5]}.
According to various compression factors, there are three versions of FF++ dataset: c0 (raw), c23 (light compression), and c40 (heavy compression).
Our experiments are mainly conducted on the c40 version, the most challenging case.
As for dataset preprocessing, we sample 20 frames from each manipulated video and 80 frames from each original video.
Compared to the setting of [1]}, the number of frames we use for training is pretty less.
Besides, we utilize retinaface[7]} to detect faces in each frame.
